Thailand's army has accused Cambodia of breaching a newly-signed ceasefire deal reached after weeks of deadly clashes that forced nearly one million people from their homes. In a statement, the Thai army said than more than 250 unmanned aerial vehicles (UAVs) were detected flying from the Cambodian side on Sunday night. The ceasefire took effect at noon local time (05:00 GMT) on Saturday. Both sides agreed to freeze the front lines where they are now, ban reinforcements and allow civilians living in border areas to return as soon as possible. It had been seen as a breakthrough, which came after days of talks between both countries, with diplomatic encouragement from China and the US.

Current techniques for detecting AI-generated text are largely confined to manual feature crafting and supervised binary classification paradigms. These methodologies typically lead to performance bottlenecks and unsatisfactory generalizability. Consequently, these methods are often inapplicable for out-of-distribution (OOD) data and newly emerged large language models (LLMs). In this paper, we revisit the task of AI-generated text detection. We argue that the key to accomplishing this task lies in distinguishing writing styles of different authors, rather than simply classifying the text into human-written or AI-generated text.

The meteoric rise in text generation capability has been accompanied by parallel growth in interest in machine-generated text detection: the capability to identify whether a given text was generated using a model or written by a person. While detection models show strong performance, they have the capacity to cause significant negative impacts. We explore potential biases in English machine-generated text detection systems. We curate a dataset of student essays and assess 16 different detection systems for bias across four attributes: gender, race/ethnicity, English-language learner (ELL) status, and economic status. We evaluate these attributes using regression-based models to determine the significance and power of the effects, as well as performing subgroup analysis. We find that while biases are generally inconsistent across systems, there are several key issues: several models tend to classify disadvantaged groups as machine-generated, ELL essays are more likely to be classified as machine-generated, economically disadvantaged students' essays are less likely to be classified as machine-generated, and non-White ELL essays are disproportionately classified as machine-generated relative to their White counterparts. Finally, we perform human annotation and find that while humans perform generally poorly at the detection task, they show no significant biases on the studied attributes.

Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. grand jury now probing mystery of dead teen stuffed in trunk of D4vd's Tesla, sources say D4vd (David Anthony Burke) performs at the Bonnaroo Music and Arts Festival in Manchester, Tennessee, in June 2024. This is read by an automated voice. Please report any issues or inconsistencies here . A Los Angeles County grand jury is hearing evidence related to the death of a teenage girl whose body was discovered stuffed inside the trunk of singer D4vd's Tesla earlier this year, two law enforcement sources told The Times.

Botanists say detectives are overlooking a potentially vital source of crime scene evidence. Breakthroughs, discoveries, and DIY tips sent every weekday. Moss is one of the world's oldest and most basic plants. Part of the bryophyte family, the estimated 12,000 known moss species have evolved over millions of years to flourish without seeds, leaves, stems, or even roots. This allows the sturdy plants to absorb all their water and nutrients from the environment around them.

This research explores the evolution of the detective archetype in French detective fiction through computational analysis. Using quantitative methods and character-level embeddings, we show that a supervised model is able to capture the unity of the detective archetype across 150 years of literature, from M. Lecoq (1866) to Commissaire Adamsberg (2017). Building on this finding, the study demonstrates how the detective figure evolves from a secondary narrative role to become the central character and the "reasoning machine" [35] of the classical detective story. In the aftermath of the Second World War, with the importation of the hardboiled tradition into France, the archetype becomes more complex, navigating the genre's turn toward social violence and moral ambiguity.

The rapid advancement of large language models (LLMs) such as ChatGPT, DeepSeek, and Claude has significantly increased the presence of AI-generated text in digital communication. This trend has heightened the need for reliable detection methods to distinguish between human-authored and machine-generated content. Existing approaches both zero-shot methods and supervised classifiers largely conceptualize this task as a binary classification problem, often leading to poor generalization across domains and models. In this paper, we argue that such a binary formulation fundamentally mischaracterizes the detection task by assuming a coherent representation of human-written texts. In reality, human texts do not constitute a unified distribution, and their diversity cannot be effectively captured through limited sampling. This causes previous classifiers to memorize observed OOD characteristics rather than learn the essence of `non-ID' behavior, limiting generalization to unseen human-authored inputs. Based on this observation, we propose reframing the detection task as an out-of-distribution (OOD) detection problem, treating human-written texts as distributional outliers while machine-generated texts are in-distribution (ID) samples. To this end, we develop a detection framework using one-class learning method including DeepSVDD and HRN, and score-based learning techniques such as energy-based method, enabling robust and generalizable performance. Extensive experiments across multiple datasets validate the effectiveness of our OOD-based approach. Specifically, the OOD-based method achieves 98.3% AUROC and AUPR with only 8.9% FPR95 on DeepFake dataset. Moreover, we test our detection framework on multilingual, attacked, and unseen-model and -domain text settings, demonstrating the robustness and generalizability of our framework. Code, pretrained weights, and demo will be released.

Karen Price was just 15 when she vanished in 1981 and, had it not been for a chance discovery by two builders, her body might never have been found. Because no-one was looking for her. Dubbed Little Miss Nobody, Karen had not been seen for eight years when her skeletal remains, wrapped in a carpet, were uncovered by two unsuspecting builders in Cardiff city centre on 7 December 1989. Her body, found in a shallow grave outside a basement flat on Fitzhamon Embankment, was so badly decomposed it was impossible to establish the cause of her death. Now, more than 40 years on and after the release of her killer, a new documentary has examined how police put together the jigsaw to solve the killing of a teenager known to no-one and how it involved groundbreaking methods to bring two men to justice.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A killer targeted men using Grindr, police say. The Grindr logo is seen among other dating apps on a mobile phone screen. This is read by an automated voice. Please report any issues or inconsistencies here .